Automatic Dependent Surveillance Research Articles

Global aviation contrail climate effects from 2019 to 2021

Abstract. The current best-estimate of the global annual mean radiative forcing (RF) attributable to contrail cirrus is thought to be 3 times larger than the RF from aviation's cumulative CO2 emissions. Here, we simulate the global contrail RF for 2019–2021 using reanalysis weather data and improved engine emission estimates along actual flight trajectories derived from Automatic Dependent Surveillance–Broadcast telemetry. Our 2019 global annual mean contrail net RF (62.1 mW m−2) is 44 % lower than current best estimates for 2018 (111 [33, 189] mW m−2, 95 % confidence interval). Regionally, the contrail net RF is largest over Europe (876 mW m−2) and the USA (414 mW m−2), while the RF values over East Asia (64 mW m−2) and China (62 mW m−2) are close to the global average, because fewer flights in these regions form persistent contrails resulting from lower cruise altitudes and limited ice supersaturated regions in the subtropics due to the Hadley Circulation. Globally, COVID-19 reduced the flight distance flown and contrail net RF in 2020 (−43 % and −56 %, respectively, relative to 2019) and 2021 (−31 % and −49 %, respectively) with significant regional variations. Around 14 % of all flights in 2019 formed a contrail with a net warming effect, yet only 2 % of all flights caused 80 % of the annual contrail energy forcing. The spatiotemporal patterns of the most strongly warming and cooling contrail segments can be attributed to flight scheduling, engine particle number emissions, tropopause height, and background radiation fields. Our contrail RF estimates are most sensitive to corrections applied to the global humidity fields, followed by assumptions on the engine particle number emissions, and are least sensitive to radiative heating effects on the contrail plume and contrail–contrail overlapping. Using this sensitivity analysis, we estimate that the 2019 global contrail net RF could range between 34.8 and 74.8 mW m−2.

Delay Prediction of Aircraft Using Machine Learning Classifiers

Accurate flight delay prediction is fundamental to establish the more efficient airline business. Recent studies have been focused on applying machine learning methods to predict the flight delay. Most of the previous prediction methods are conducted in a single route or airport. This paper explores a broader scope of factors which may potentially influence the flight delay, and compares several machine learning-based models in designed generalized flight delay prediction tasks. To build a dataset for the proposed scheme, automatic dependent surveillance broadcast (ADS-B) messages are received, pre-processed, and integrated with other information such as weather condition, flight schedule, and airport information. The designed prediction tasks contain different classification tasks and a regression task. Experimental results show that long short-term memory (LSTM) is capable of handling the obtained aviation sequence data, but over fitting problem occurs in our limited dataset. Compared with the previous schemes, the proposed random forest-based model can obtain higher prediction accuracy (90.2% for the binary classification) and can overcome the over fitting problem.

Domestic and international aviation emission inventories for the UNFCCC parties

Global aviation emissions have been growing despite international efforts to limit climate change. Quantifying the status quo of domestic and international aviation emissions is necessary for establishing an understanding of current emissions and their mitigation. Yet, a majority of the United Nations framework convention on climate change (UNFCCC)-ratifying parties have infrequently disclosed aviation emissions within the international framework, if at all. Here, we present a set of national aviation emission and fuel burn inventories for these 197 individual parties, as calculated by the high-resolution aviation transport emissions assessment model (AviTeam) model. In addition to CO2 emissions, the AviTeam model calculates pollutant emissions, including NOx, SOx, unburnt hydrocarbons, black carbon, and organic carbon. Emission inventories are created in aggregated and gridded format and rely on Automatic Dependent Surveillance–Broadcast combined with schedule data. The cumulative global fuel burn is estimated at 291 Tg for the year 2019. This corresponds to CO2 emissions of 920 Tg, with 306 Tg originating from domestic aviation. We present emissions from 151 countries that have yet to report their emissions for 2019, which sum to 417 TgCO2. The improved availability of national emissions data facilitated by this inventory could support mitigation efforts in developed and developing countries and shows that such tools could bolster sector reporting to the UNFCCC.

A Radio Frequency Fingerprinting-Based Aircraft Identification Method Using ADS-B Transmissions

The automatic dependent surveillance broadcast (ADS-B) system is one of the key components of the next generation air transportation system (NextGen). ADS-B messages are transmitted in unencrypted plain text. This, however, causes significant security vulnerabilities, leaving the system open to various types of wireless attacks. In particular, the attacks can be intensified by simple hardware, like a software-defined radio (SDR). In order to provide high security against such attacks, radio frequency fingerprinting (RFF) approaches offer reasonable solutions. In this study, an RFF method is proposed for aircraft identification based on ADS-B transmissions. Initially, 3480 ADS-B samples were collected by an SDR from eight aircrafts. The power spectral density (PSD) features were then extracted from the filtered and normalized samples. Furthermore, the support vector machine (SVM) with three kernels (linear, polynomial, and radial basis function) was used to identify the aircraft. Moreover, the classification accuracy was demonstrated via varying channel signal-to-noise ratio (SNR) levels (10–30 dB). With a minimum accuracy of 92% achieved at lower SNR levels (10 dB), the proposed method based on SVM with a polynomial kernel offers an acceptable performance. The promising performance achieved with even a small dataset also suggests that the proposed method is implementable in real-world applications.

Development of a machine learning model for predicting abnormalities of commercial airplanes

Airplanes are a social necessity for movement of humans, goods, and other. They are generally safe modes of transportation; however, incidents and accidents occassionally occur. To prevent aviation accidents, it is necessary to develop a machine-learning model to detect and predict commercial flights using automatic dependent surveillance–broadcast data. This study combined data-quality detection, anomaly detection, and abnormality-classification-model development. The research methodology involved the following stages: problem statement, data selection and labeling, prediction-model development, deployment, and testing. The data labeling process was based on the rules framed by the international civil aviation organization for commercial, jet-engine flights and validated by expert commercial pilots. The results showed that the best prediction model, the quadratic-discriminant-analysis, was 93% accurate, indicating a “good fit.” Moreover, the model's area-under-the-curve results for abnormal and normal detection were 0.97 and 0.96, respectively, thus confirming its “good fit.”

Fuel Burn Method Assessment Using Automatic Dependent Surveillance–Broadcast and European Reanalysis Data: Limited Flight Sample Analysis

Fuel burn during the actual route flown is an important indicator of aircraft operational efficiency. This study aims to assess and systematically evaluate the method for fuel consumed during flights using data from the automatic dependent surveillance–broadcast (ADS-B), European reanalysis (ERA5) meteorological dataset, and BADA 3 performance. A literature background and comprehensive methodology are provided for fuel estimation using track data. The airborne part of the trajectory was used to estimate the total trip fuel consumed during several flights of a commercial airliner. The calculated fuel burn is compared with measured fuel consumption from the flight data recorder (FDR). The results show that fuel consumption for the entire airborne part of the trajectory can be estimated with an average error of 1.2% and with a standard deviation of 1.3%. Detailed results of fuel burn for individual flight phases, from the initial climb to the approach, are also presented. In addition, this paper also discusses the sources of errors and the potential applications of the method for network operations and environmental monitoring.

Hidden Markov Models and Flight Phase Identification

The use of Hidden Markov Models (HMMs) in segmenting flight phases is a compelling approach with significant implications for aviation and aerospace research. It leverages the temporal sequences of flight data to delineate various phases of an aircraft’s journey, making it a valuable tool for enhancing the anal- ysis of flight performance and safety. In this work, we implement a multivariate HMM to identify 6 flight phases: taxi, takeoff, climb, cruise, approach and rollout. We reach a median global accuracy of about 97% over a sample of several thousand flights with a very low number of decoded unlikely transitions. Regarding several performance metrics, our method is competitive with existing methods in the litera- ture, such as fuzzy logic. Additionally, it provides, for each point of the flight, a probability of belonging to each phase. Even in situations where there are missing values in the data, HMMs remain effective, ensuring that no critical information is lost during the segmentation process. We show that HMMs work seamlessly with the fine granularity of Flight Data Recorder (FDR) data. HMMs offer remarkable flex- ibility and adaptability, proving particularly effective when the number or order of phases is unknown or not predetermined, as is often the case with complex flight scenarios such as helicopter flights. This adaptability is crucial for handling the diverse range of flight operations that differ from one aircraft to another. An example is given with the segmentation of an Automatic Dependent Surveillance–Broadcast (ADS-B) helicopter flight operated by the Swedish National Police.

The high-resolution Global Aviation emissions Inventory based on ADS-B (GAIA) for 2019–2021

Abstract. Aviation emissions that are dispersed into the Earth's atmosphere affect the climate and air pollution, with significant spatiotemporal variation owing to heterogeneous aircraft activity. In this paper, we use historical flight trajectories derived from Automatic Dependent Surveillance–Broadcast (ADS-B) telemetry and reanalysis weather data for 2019–2021 to develop the Global Aviation emissions Inventory based on ADS-B (GAIA). In 2019, 40.2 million flights collectively travelled 61 billion kilometres using 283 Tg of fuel, leading to CO2, NOX and non-volatile particulate matter (nvPM) mass and number emissions of 893 Tg, 4.49 Tg, 21.4 Gg and 2.8 × 1026 respectively. Global responses to COVID-19 led to reductions in the annual flight distance flown and CO2 and NOX emissions in 2020 (−43 %, −48 % and −50 % respectively relative to 2019) and 2021 (−31 %, −41 % and −43 % respectively), with significant regional variability. Short-haul flights with durations < 3 h accounted for 83 % of all flights but only for 35 % of the 2019 CO2 emissions, while long-haul flights with durations > 6 h (5 % of all flights) were responsible for 43 % of CO2 and 49 % of NOX emissions. Globally, the actual flight trajectories flown are, on average, ∼ 5 % greater than the great circle path between the origin and destination airports, but this varies by region and flight distance. An evaluation of 8705 unique flights between London and Singapore showed large variabilities in the flight trajectory profile, fuel consumption and emission indices. GAIA captures the spatiotemporal distribution of aviation activity and emissions and is provided for use in future studies to evaluate the negative externalities arising from global aviation.

SoK of Machine Learning and Deep Learning Based Anomaly Detection Methods for Automatic Dependent Surveillance- Broadcast

SoK of Machine Learning and Deep Learning Based Anomaly Detection Methods for Automatic Dependent Surveillance- Broadcast

PENGEMBANGAN FLIGHT MONITORING SYSTEM MODE-S BERBASIS OFFLINE MEWUJUDKAN KEMANDIRIAN TEKNOLOGI PENERBANGAN

Technological independence is important for every country in global competition. One of the technological advancements developed by the Indonesian government through BPPT is Automatic Dependent Surveillance Broadcast (ADS-B) type in, which functions as a flight monitoring system. The Indonesian Air Force (TNI AU) through Depohar 50 (Depot Maintenance 50) has also developed ADS-B type in, which is currently deployed in six radar units known as the Flight Monitoring System (FMS) Mode-S. Accordance with government regulations (Minister of Transportation Regulation No. 81 of 2017), the government has mandated the use of ADS-B for flight safety. ADS-B has two types, in and out. In the TNI AU, ADS-B out is installed on aircraft such as Grob, KT-1, and Cessna, while ADS-B in is installed on Weibel radar. However, since ADS-B is not yet installed on TNI AU aircraft, flight data from the Air Traffic Control (ATC) was communicated to the pilots, hence, they can be aware of the air traffic situation. Furthermore, due to the increasing air traffic, communication with ATC has become limited. A qualitative method was used in the development of the ADS-B type in TNI AU aircraft, and the previous ADS-B type in development could only be installed on the ground station. The information received from ADS-B on the aircraft was real-time air traffic, which helps pilots make decisions to enhance flight safety, such as reducing the risk of aircraft collisions. The use of this technology on TNI AU aircraft is important for improving flight safety and technological independence.

FLIGHT DELAY PREDICTION BASED ON AVIATION BIG DATA AND MACHINE LEARNING

Accurate flight delay prediction is fundamental to establish the more efficient airline business. Recent studies have been focused on applying machine learning methods to predict the flight delay. Most of the previous prediction methods are conducted in a single route or airport. This paper explores a broader scope of factors which may potentially influence the flight delay, and compares several machine learning-based models in designed generalized flight delay prediction tasks. To build a dataset for the proposed scheme, automatic dependent surveillance broadcast (ADS-B) messages are received, pre-processed, and integrated with other information such as weather condition, flight schedule, and airport information. The designed prediction tasks contain different classification tasks and a regression task. Experimental results show that long short-term memory (LSTM) is capable of handling the obtained aviation sequence data, but over fitting problem occurs in our limited dataset. Compared with the previous schemes, the proposed random forest-based model can obtain higher prediction accuracy (90.2% for the binary classification) and can overcome the over fitting problem. KEYWORDS: Automatic Dependent Surveillance-Broadcast (ADS-B), Air Traffic Flow Management (ATFM) , Random Forest (RF), K-nearest Neighbors (K-NN)

Risk Assessment Procedure of Final Approach to Landing Using Deep Learning

Flight safety is the highest priority of all involved in aviation. All civil aviation authorities around the world have the responsibility to guarantee flight safety, but the flight data are considered confidential and regarded as trade secrets by airlines. Therefore, the publicly available sources are employed in the study to investigate the risk and related factors of flight safety during the approach to landing. Automatic Dependent Surveillance–Broadcast flight trajectory data and the Meteorological Terminal Aviation Routine Weather Report are used to analyze the risk associated with the final approach to landing. This study presents a procedure to conduct the data mining and risk assessment of flights landing at Taipei Songshan Airport (TSA). A deep learning method is adopted to tackle the problems associated with nonlinear regression that cannot be addressed by traditional statistical regression methods. The trajectory and glide path angle deviations of each flight are determined and applied to the model training. These two deviation variables are regarded as the evaluation standards, while the weather conditions are used as risk factors. Each weather factor in the deep learning models is weighted by the permutation feature importance analysis to capture how much influence each factor has on predictions from the model. The results show that the estimation accuracy of trajectory deviation with the 10-input model and glide path angle deviation with the 11-input model is over 90 and 95%, respectively. The trajectory deviations of runway 10 have higher impacts from temperature, precipitations, and crosswind.

Aircraft trajectory prediction and aviation safety in ADS-B failure conditions based on neural network

With the rapid expansion of transportation demand, the number of global flights has rapidly increased, which also poses challenges to air traffic management (ATM). Considering that the radar system in ATM can no longer meet the requirements of flight safety, a very promising next-generation air traffic control technology—Automatic Dependent Surveillance Broadcast (ADS-B) technology has been introduced. However, in the event of on-board equipment failure and local area signal interference, the ADS-B’s signal will disappear or be interrupted. This sudden situation can pose a danger to aviation safety. To solve this problem, this article proposes a bidirectional long short-term memory (Bi-LSTM) network prediction method combining historical ADS-B data to short-term predict the trajectory of aircraft, which can improve aviation safety in busy airspace. Firstly, the problem of frequent dynamic modeling of different types of aircraft was solved by utilizing historical ADS-B data as the data source. Secondly, the data cleansing method is proposed for ADS-B raw data. Furthermore, considering that the spatial trajectory of the aircraft is a complex time series with continuity and interactivity, a bidirectional LSTM based aircraft trajectory prediction framework is proposed to further improve prediction accuracy. Finally, a trajectory with frequent changes was selected for prediction, and compared with 7 prediction methods. The results showed that the proposed method had high prediction accuracy, thus also improving the aviation safety of the aircraft.

A First Look at Exploiting the Automatic Dependent Surveillance-Contract Protocol for Open Aviation Research

The OpenSky Network has accumulated air traffic data for research for over a decade, with sensor registrations increasing from a few to over 6000. Recent enhancements have included the addition of data sources such as Mode S, FLARM, and VHF to the initial collection, which started only with the Automatic Dependent Surveillance - Broadcast (ADS-B) technology. However, the growth of the crowdsourced network has predominantly occurred in developed nations, leaving extensive inhabited regions, but also remote areas such as oceans and mountains, with lacking coverage.
 To address this issue, the deployment of Automatic Dependent Surveillance - Contract (ADS-C) technology offers a potential remedy. ADS-C is an advanced surveillance system that utilizes an aircraft's onboard systems to automatically transmit crucial information, including position, altitude, speed, navigation intentions, and meteorological data. Different from ADS-B, ADS-C transmits contract data via satellite to specific Air Traffic Services Units (ATSU) or Aeronautical Operational Control (AOC) facilities, contributing to a more comprehensive and global approach to air traffic monitoring.
 In this paper, we describe the background of ADS-C and implement a resource-intensive data collection. We analyse 227,126 messages collected over 4 months and find that they can be an excellent complementary data source for researchers working with aviation data.

Flight Deck Simulation of Complex Clearance Information Entry During Relative Spacing Arrival Operations

Interval management (IM) uses Automatic Dependent Surveillance–Broadcast to provide greater air traffic system throughput and efficiency by improving inter-aircraft spacing precision. The IM Dependent Staggered Approach (DSA) application enables aircraft within a pair to achieve a spacing objective when arriving to parallel, dependently scheduled runways. These operations may require lengthier or more complex IM clearance communications that, coupled with manual entry of ownship route information and forecast winds, can result in a more challenging setup task than has been previously examined. An experiment with 16 pilots was conducted to examine the flight crew acceptability, feasibility, and technical assumptions related to conducting IM DSA operations in a metering environment with voice communications. Results show flight crew errors during entry of the clearance elements into the Cockpit Display of Traffic Information traffic display, especially with a long and highly complex IM clearance. The manual entry of ownship route and forecast wind information also appeared to increase the complexity of the entry task and the potential for errors. Recommendations are made to the Federal Aviation Administration and equipment manufacturers regarding IM communications and the design of related flight deck displays to avoid entry errors and, when they occur, indicate the source to pilots.

Is Safety Always Going to be More Important Than Privacy?

Lately Elon Musk published headlines news that has been focused on his private jet and its whereabouts, leading the billionaire to announce he is taking legal action against a 20-year-old who has been tracking his travels. The US Federal Aviation Administration is proposing an upgrade to air transportation that will fundamentally overhaul the current, aging system. A key component, the automatic dependent surveillance broadcast (ADS-B) system, will enhance air traffic monitoring and control by requiring aircraft to continually broadcast position, identity and velocity via unencrypted data links to ground stations. Although ADS-B may enhance air traffic safety and support the increase in traffic demands, open broadcast of clear aircraft data points raise serious security concerns. The ability to encrypt ADS-B message transactions would afford protection to ensure that the confidentiality of aircraft data is not compromised. The implementation of an encryption framework for a large, distributed and dynamic system, however, is nontrivial. This paper discusses the ADS-B as a tool — the way that it is beneficial and the way it could be exploited.

Dump1030: Open-Source Plug-and-Play Demodulator/Decoder for 1030 MHz Uplink

Automatic Dependent Surveillance (ADS), Automatic Dependent Surveillance-Broadcast (ADS-B), Secondary Surveillance Radars (SSR), and Mode S are key air surveillance technologies representing a critical component of next-generation air transportation systems. However, compared to 1090MHz demodulators and decoders, which have plenty of implementations, the 1030MHz uplink receivers are, in general, scarcely, if at all, represented. In this paper, we present the development and evaluation of dump1030 – cross-platform plug-and-play open-source implementation for decoding 1030MHz uplink Mode A/C/S interrogations. We demonstrate and detail an agile development process of building dump1030 by adapting a state-of-the-art dump1090 design and implementation. In our repeated experiments, dump1030 achieves a high detection accuracy of 1030MHz interrogation signals based on lab evaluation using synthetically-generated interrogation signals. We also discuss a handful of practical use cases where dump1030 can find immediate application and implementation, both in research and industrial settings.

Computerized Ionospheric Tomography Based on the ADS-B System

The broadcast automatic dependent surveillance (ADS-B) system is a new-generation air traffic control system designed to avoid the waste of resources in secondary radars. The establishment of the spaceborne ADS-B system provides a broad prospect for ionospheric tomography. In this paper, the external observation information of the ionosphere is obtained by measuring the Faraday rotation angle, that is, the total electron content (TEC). Tomography research can be carried out all over the world to conduct large-scale ionospheric electron density research. The experiment selected two different regions and had a time resolution of two hours, a height resolution of 200 km, a latitude resolution of 2°, and a longitude resolution of 5°. Based on the simulated spaceborne ADS-B signal to invert the regional ionospheric electron density, the latitude, longitude, and height distributions of inversion result are basically consistent with those of the actual ionospheric electron density.

Evaluation of Extended Projected Profiles impact on vertical trajectory prediction

The future Air Traffic Management System (ATM) is based on the concept of Trajectory Based Operations (TBO), in which all stakeholders have access to a consistent and synchronised view of flight trajectories. In the TBO framework, the trajectory prediction plays a key role. Inaccuracy of current trajectory predictors caused by the lack of a common understanding of stakeholders’ intents can be reduced by the sharing of information by airspace users. A step towards a shared information network is the Extended Projected Profile (EPP), downlinked from airborne to ground via automatic dependent surveillance contract (ADS-C). In summary, it contains the flight intent from the Flight Management System (FMS) and other information. This paper explores the EPP messages and the possible performance enhancement they introduce in the predictions of the top of descent (TOD) and the descent vertical profile. 1

Validation of a Contrail Life-Cycle Model in Central Europe

In an industry beset by economic and environmental crises, air transport, the safest and most efficient long-haul mode of transport, is confronted daily with multi-criteria challenges to improve its environmental performance. The formation of contrails through the emission of water vapor and condensation nuclei in what are actually dry and clean atmospheric layers represents one of the most unpredictable, or measurable, environmental impacts of air traffic. Following the bottom-up principle to evaluate individual contrails in order to derive recommendations for trajectory optimization, not only the calculation of the radiative forcing of the contrails but also the modeling of their life cycle is burdened with uncertainties. In former studies for modeling the microphysical life cycle of contrails based on a 3-D Gaussian plume model, the atmospheric conditions, specifically the turbulence, were often unknown and had to be considered as a free input variable. In this study, an innovative photographic method for identifying and tracking contrails in Central Europe, connected with database access to Automatic Dependent Surveillance—Broadcast (ADS-B) data (i.e., aircraft type, speed, altitude, track, etc.), and a combination of measured and modeled weather data are used to validate the contrail life-cycle model (i.e., the assumed Gaussian plume behavior). We found that it is challenging to model the position of ice-supersaturated layers with global forecast models, but they have the most significant impact on the contrail lifetime. On average, the contrail’s lifespan could be modeled with an error margin of 10%. Sometimes, we slightly underestimated the lifetime. With the validated and plausible contrail life-cycle model, we can apply the climate effectiveness of individual contrails with higher certainty in trajectory optimization and compare it, for example, with economic aspects such as delay costs or fuel costs.